Intelligent sensing systems have shown remarkable performance on many environmental perception (e.g., liquid recognition [1], soil moisture estimation [2], temperature monitoring [3]) and human activity (e.g., fall detection [4], vital sign estimation [5], location tracking [6]) tasks, becoming the core component of smart physical-related services, such as smart city and smart manufacturing. However, the current cost of designing intelligent sensing systems is relatively high since the models were designed to solve specific tasks with expensive expert knowledge [7] or a substantial amount of domain-specific data [8], one at a time. Foundation models [9] - the latest generation of artificial intelligence (AI) models - are intuitively used to generalize the model for numerous downstream tasks, which are trained on large multimodal datasets. They can solve entirely new tasks which the models are never explicitly trained for. Although the foundation models paradigm perform well in computer vision or natural language processing area, applying them in the intelligent sensing area is still challenging for two reasons. First, it is difficult to generate or access massive and diverse sensing datasets. Massive high-quality data is crucial for foundation model applications, such as computer vision [10] and natural language processing [9]. However, this requirement is often unmet in the sensing field.

Link prediction typically studies the probability of future interconnection among nodes with the observation in a single social network. More often than not, real scenario is presented as a multiplex network with common (anchor) users active in multiple social networks. In the literature, most existing works study either the intra-link prediction in a single network or inter-link prediction among networks (a.k.a. network alignment), and consider two learning tasks are independent from each other, which is still away from the fact. On the representation space, the vast majority of existing methods are built upon the traditional Euclidean space, unaware of the inherent geometry of social networks. The third issue is on the scarce anchor users. Annotating anchor users is laborious and expensive, and thus it is impractical to work with quantities of anchor users. Herein, in light of the issues above, we propose to study a challenging yet practical problem of Geometry-aware Collective Link Prediction across Multiplex Network. To address this problem, we present a novel contrastive model, RCoCo, which collaborates intra- and inter-network behaviors in Riemannian spaces. In RCoCo, we design a curvature-aware graph attention network ($\kappa-$GAT), conducting attention mechanism in Riemannian manifold whose curvature is estimated by the Ricci curvatures over the network. Thereafter, we formulate intra- and inter-contrastive loss in the manifolds, in which we augment graphs by exploring the high-order structure of community and information transfer on anchor users. Finally, we conduct extensive experiments with 14 strong baselines on 8 real-world datasets, and show the effectiveness of RCoCo.